In this task, we are interested in auditing if the model has learned some salient property allowing it to predict the gps coordinates of a given input image.
We do so by coping with the problem of geolocalization predicting the GPS coordinates of images taken from all over the world.
Such images exhibit a wide range of variations, picturing different objects, using different camera settings, taken at different daytime or seasons.
Moreover, the images provide very few visual clues about the respective GPS location.
Unlike previous works [1]}, [2]}, [3]}, [4]}, we neither make prior assumptions on the task nor simplify the problem by restricting the task to images from famous landmarks and cities, natural areas like deserts or mountains.
We want to test if the model works at a global scale without any assumptions on the data or task.
